
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "167"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 124 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 119 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.710893	6.301431	12.936009	9.686926	12.053846	25.285677	16.875952	17.508894	0.6710	0.6468	0.1444	12.802775	7.509326
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	23.866913	20.913294	15.963782	18.950139	34.956373	30.803573	58.088965	23.576756	0.5725	0.5533	0.1157	5.347863	3.544493
2459592	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	1.651475	0.600311	48.536677	55.477122	37.885316	30.106465	12.106827	8.555803	0.5818	0.6251	0.2505	nan	nan
2459591	digital_ok	100.00%	0.00%	4.86%	0.00%	100.00%	0.00%	30.484044	24.335555	18.176512	22.856177	21.609129	28.284316	18.437114	16.132078	0.5030	0.5035	0.1711	3.556889	2.672152
2459590	digital_ok	100.00%	0.54%	4.86%	0.00%	100.00%	0.00%	26.209181	26.137947	16.999013	19.740970	21.625790	28.311456	45.738703	29.302424	0.5278	0.5039	0.1921	5.048707	3.415415
2459589	digital_ok	100.00%	5.94%	5.40%	0.00%	100.00%	0.00%	28.642050	26.041231	13.859954	16.116152	18.124457	20.130986	20.842017	15.899007	0.5640	0.5417	0.1433	5.100142	3.288202
2459588	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	16.064207	20.763280	19.483500	21.396399	25.431392	28.689526	15.143632	20.512816	0.6171	0.5802	0.1244	8.351747	4.645002
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	24.530660	27.194729	27.375796	31.411775	2.469128	4.972816	13.193223	31.428329	nan	nan	nan	5.877902	4.374129
2459586	digital_ok	100.00%	17.30%	3.78%	0.00%	100.00%	0.00%	39.340057	22.358476	1.490526	1.904547	11.556675	11.318021	75.436089	36.054995	0.4822	0.5054	0.1585	3.575240	2.856197
2459585	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.741865	22.174127	18.862903	22.499537	9.728925	9.464957	44.741712	28.480335	0.5317	0.5815	0.1682	0.000000	0.000000
2459584	digital_ok	100.00%	63.39%	51.62%	0.00%	100.00%	0.00%	59.606095	37.484714	22.539544	32.661703	12.985863	15.459672	59.774787	47.798659	0.4220	0.4550	0.1565	3.635780	4.329284
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	digital_ok	100.00%	15.14%	0.00%	0.00%	100.00%	0.00%	43.126258	22.644077	5.779984	8.793763	21.561679	24.276491	33.431943	11.783116	0.4756	0.5175	0.1930	3.191825	2.781079
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	36.925117	31.208882	7.090272	9.469837	15.646993	16.927977	48.472061	12.098237	0.5046	0.5152	0.1779	3.556910	3.068633
2459580	digital_ok	100.00%	2.70%	0.54%	0.00%	100.00%	0.00%	38.239321	23.347992	7.041175	9.902292	18.012527	20.662900	73.247568	23.973898	0.4840	0.5145	0.1807	4.967485	4.075511
2459579	digital_ok	100.00%	27.55%	10.80%	0.00%	100.00%	0.00%	47.613793	21.771210	7.721101	11.614940	24.603444	25.354118	25.177001	18.333883	0.4699	0.5045	0.1987	5.771562	4.084161
2459578	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	2.203603	1.037098	26.389645	30.883294	48.880644	34.604762	161.219476	93.334503	0.5726	0.6532	0.2363	nan	nan
2459577	digital_ok	100.00%	0.00%	2.16%	0.00%	100.00%	0.00%	35.883746	31.395424	6.735675	8.609234	20.269061	19.716920	54.788369	24.604644	0.5009	0.5100	0.1821	4.163789	3.407131
2459576	digital_ok	100.00%	5.94%	0.00%	0.00%	100.00%	0.00%	40.034899	21.989257	4.076088	6.163762	21.168623	18.692770	84.124061	60.310452	0.4932	0.5242	0.1609	3.234331	2.716440
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	46.734968	22.193829	4.229756	6.783928	14.511009	15.389311	9.071717	6.726440	0.5880	0.6033	0.1507	3.374387	3.065170
2459574	digital_ok	100.00%	2.16%	12.45%	0.00%	100.00%	0.00%	20.826093	22.376518	5.198499	5.978196	17.586057	22.136951	31.193683	20.092826	0.5393	0.5190	0.1874	4.214995	2.962136
2459573	digital_ok	100.00%	17.31%	0.00%	0.00%	100.00%	0.00%	46.268749	23.324596	8.109540	13.016640	29.451843	21.804490	37.486253	15.274941	0.4848	0.5258	0.1913	3.202867	2.816697
2459572	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	26.703165	24.212700	5.199219	6.209372	14.770473	13.306006	26.177574	6.823218	0.5317	0.5378	0.1802	5.173780	4.213379
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	nan	nan
2459570	digital_ok	100.00%	29.73%	20.00%	0.00%	100.00%	0.00%	18.791754	12.550634	-0.906935	-0.543379	3.716067	0.801579	11.185165	2.335956	0.4792	0.5276	0.1665	0.000000	0.000000
2459569	digital_ok	100.00%	3.79%	0.00%	0.00%	100.00%	0.00%	37.877471	22.449109	4.918099	7.557184	13.820264	10.263200	12.106114	8.634861	0.6418	0.6402	0.1104	6.526060	4.137133
2459566	digital_ok	100.00%	1.08%	0.00%	0.00%	100.00%	0.00%	28.217287	19.301325	6.950158	9.201557	19.361939	18.173671	17.650249	11.029146	0.5144	0.5238	0.1901	3.318738	2.720929
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	20.24%	2.31%	0.00%	100.00%	0.00%	41.646406	18.350533	-0.376425	0.365480	1.823314	2.232678	40.931201	9.991918	0.4689	0.5326	0.1455	2.282161	2.733173
2459563	digital_ok	100.00%	11.35%	0.00%	0.00%	100.00%	0.00%	33.943253	23.916929	14.752659	19.365729	18.972464	19.229250	61.414063	19.952393	0.5221	0.5273	0.2067	2.800558	2.616640
2459562	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	32.486266	10.633264	-0.434009	0.106518	3.642284	1.253752	17.915908	3.028709	0.5182	0.5811	0.2541	2.630111	3.005097
2459561	digital_ok	100.00%	3.24%	0.00%	0.00%	100.00%	0.00%	23.689400	15.420810	-0.546181	0.614414	2.938904	2.596633	47.135594	13.254404	0.5411	0.5378	0.2496	3.565242	3.032806
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459559	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	35.461627	12.456828	-0.200004	0.892216	2.851281	2.606538	20.766869	9.287513	0.5236	0.5748	0.2543	2.614969	3.012298
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	21.008240	24.364935	9.265899	10.389568	17.259202	20.791794	75.968222	28.552741	0.5787	0.5301	0.2754	6.187807	4.331365
2459557	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7195	0.7147	0.2033	nan	nan
2459556	digital_ok	100.00%	8.65%	0.00%	0.00%	100.00%	0.00%	37.485354	25.911573	8.121740	10.380125	20.874398	16.607533	158.528739	21.117789	0.5166	0.5294	0.2310	4.593731	4.271223
2459554	digital_ok	-	16.21%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.4969	0.5727	0.2543	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.5157	0.5747	0.2644	nan	nan
2459552	digital_ok	100.00%	16.76%	11.35%	0.00%	100.00%	0.00%	34.139693	33.484692	11.684464	13.480266	38.547718	32.924349	59.392981	26.259465	0.5322	0.5228	0.2057	15.204135	16.015828
2459551	digital_ok	100.00%	15.13%	0.00%	0.00%	100.00%	0.00%	24.955008	15.279968	-0.676598	0.275370	4.777175	2.280847	41.697675	8.481895	0.5027	0.5427	0.1829	2.476407	2.857145
2459550	digital_ok	-	30.48%	29.65%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.3958	0.4457	0.1388	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459542	digital_ok	100.00%	14.77%	6.26%	0.00%	100.00%	0.00%	58.361740	30.888225	13.077797	20.311504	1.421094	1.558896	34.932662	29.682247	0.5673	0.6010	0.1132	3.811829	3.907118
2459541	digital_ok	100.00%	21.25%	17.00%	0.45%	100.00%	0.00%	51.923163	37.604105	8.070384	10.621819	11.304870	12.076623	29.250134	16.345715	0.5649	0.5760	0.0953	3.042966	3.460999
2459540	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.539310	14.079661	1.328301	1.960229	4.249236	4.403566	7.130727	11.087921	0.6699	0.6335	0.2261	3.717519	3.069696
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	27.907164	32.839883	15.893645	17.234023	19.410388	24.139538	24.166847	59.969235	0.5826	0.5450	0.2220	0.000000	0.000000
2459535	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.550691	10.844890	0.926644	1.672777	3.591595	4.013366	12.992928	12.357910	0.7052	0.6521	0.2510	0.809359	0.811288
2459534	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.322121	9.571957	-0.002315	1.032885	4.350416	3.740830	13.620186	36.498207	0.6905	0.6355	0.2976	-0.000000	-0.000000
2459533	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.664274	19.080355	10.290034	11.721705	13.417058	16.143736	21.273872	20.107586	0.6759	0.5847	0.3769	0.000000	0.000000
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.631502	27.052297	13.883889	14.302457	14.740896	19.887756	38.196017	19.217266	0.6804	0.5629	0.3760	9.025607	5.334202
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	100.00%	0.00%	0.00%	0.00%	-	-	2.361179	5.040793	28.372997	31.178006	30.129953	19.900269	37.498775	23.499263	0.7967	0.9176	0.2416	nan	nan
2459508	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.227419	18.130037	8.701965	8.973728	9.993192	9.996873	11.549217	11.536367	0.9032	0.9109	0.1566	0.000000	0.000000
2459505	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	20.679721	26.221580	10.385137	9.577327	3.205135	10.653943	3.065982	24.888850	0.7674	0.7709	0.1090	17.207600	18.070501
2459503	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.455333	16.646026	8.386299	10.960297	23.782251	22.543278	166.230555	29.466664	0.1080	0.1218	0.0126	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 167 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 167, Node 15:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 167: 2459650

Antenna 167: 2459649

Antenna 167: 2459648

Antenna 167: 2459647

Antenna 167: 2459646

Antenna 167: 2459645

Antenna 167: 2459642

Antenna 167: 2459640

Antenna 167: 2459639

Antenna 167: 2459638

Antenna 167: 2459637

Antenna 167: 2459636

Antenna 167: 2459635

Antenna 167: 2459634

Antenna 167: 2459633

Antenna 167: 2459632

Antenna 167: 2459631

Antenna 167: 2459630

Antenna 167: 2459629

Antenna 167: 2459628

Antenna 167: 2459627

Antenna 167: 2459626

Antenna 167: 2459625

Antenna 167: 2459624

Antenna 167: 2459623

Antenna 167: 2459622

Antenna 167: 2459621

Antenna 167: 2459620

Antenna 167: 2459619

Antenna 167: 2459618

Antenna 167: 2459617

Antenna 167: 2459616

Antenna 167: 2459615

Antenna 167: 2459614

Antenna 167: 2459613

Antenna 167: 2459612

Antenna 167: 2459611

Antenna 167: 2459610

Antenna 167: 2459609

Antenna 167: 2459608

Antenna 167: 2459607

Antenna 167: 2459605

Antenna 167: 2459604

Antenna 167: 2459603

Antenna 167: 2459602

Antenna 167: 2459598

Antenna 167: 2459597

Antenna 167: 2459596

Antenna 167: 2459595

Antenna 167: 2459594

Antenna 167: 2459593

Antenna 167: 2459592

Antenna 167: 2459591

Antenna 167: 2459590

Antenna 167: 2459589

Antenna 167: 2459588

Antenna 167: 2459587

Antenna 167: 2459586

Antenna 167: 2459585

Antenna 167: 2459584

Antenna 167: 2459583

Antenna 167: 2459582

Antenna 167: 2459581

Antenna 167: 2459580

Antenna 167: 2459579

Antenna 167: 2459578

Antenna 167: 2459577

Antenna 167: 2459576

Antenna 167: 2459575

Antenna 167: 2459574

Antenna 167: 2459573

Antenna 167: 2459572

Antenna 167: 2459571

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶